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An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

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Acta Oceanologica Sinica 2017年第11期36卷 106-114页

作者： WANG Changying CHU Jialan TAN Meng SHAO Fengjing SUI Yi LI Shujing School of Data Science and Software Engineering Qingdao UniversityQingdao 266071China Institute of Big Data Technology and Smart City of Qingdao Qingdao 266071China Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application East China Sea BranchState Oceanic AdministrationShanghai 200080China National Marine Environmental Monitoring Center State Oceanic AdministrationDalian 116023China North China Sea Data and Information Service Center North China Sea BranchState Oceanic AdministrationQingdao 266061China

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field *** this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus *** there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

关键词： automatic detection green tide adaptive threshold Landsat TM/ETM plus image

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Part-Based data Association for Visual Tracking

Part-Based Data Association for Visual Tracking

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Zhengqiang Jiang Du Q. Huynh Jian Zhang Qiang Wu School of Electrical and Data Engineering University of Technology Sydney Sydney Australia School of Computer Science and Software Engineering The University of Western Australia Perth Australia

We present a method that integrates a part-based sparse appearance model in a Bayesian inference framework for tracking targets in video sequences. We formulate the sparse appearance model as a set of smoothed colour histograms corresponding to the object windows detected by the Deformable Part Model (DPM) detector. The data association of each body part between frames is solved based on the position constraint, appearance coherence, and motion consistency. To deal with missing and noisy observations, the part detection window in the following frame is also predicted using an interacting multiple model (IMM) tracker. We have tested our tracking method on all the video sequences that involve people in upright poses from the TB-50 and TB-100 benchmark videos datasets. Our experimental results show that our tracking method outperforms six state-of-the-art tracking techniques.

关键词： Target tracking Histograms Image color analysis Adaptation models Computational modeling

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Image encryption using random sequence generated from generalized information domain

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Chinese Physics B 2016年第5期25卷 176-185页

作者：张夏衍张国基李璇任亚洲伍杰华 School of Computer Science and Engineering South China University of TechnologyHigher Education Mega Centre School of Mathematics South China University of Technology School of Software Fujian Normal University School of Computer Science and Engineering Big Data Research Center University of Electronic Science and Technology of China

A novel image encryption method based on the random sequence generated from the generalized information domain and permutation–diffusion architecture is proposed. The random sequence is generated by reconstruction from the generalized information file and discrete trajectory extraction from the data stream. The trajectory address sequence is used to generate a P-box to shuffle the plain image while random sequences are treated as keystreams. A new factor called drift factor is employed to accelerate and enhance the performance of the random sequence generator. An initial value is introduced to make the encryption method an approximately one-time pad. Experimental results show that the random sequences pass the NIST statistical test with a high ratio and extensive analysis demonstrates that the new encryption scheme has superior security.

关键词： image encryption random number generator cryptography

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Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

Deep User Modeling for Content-based Event Recommendation in...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

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A context-aware user-item representation learning for item recommendation

arXiv

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arXiv 2017年

作者： Wu, Libing Quan, Cong Li, Chenliang Wang, Qian Zheng, Bolong State Key Lab of Software Engineering School of Computer Science Wuhan University China School of Data and Computer Science Sun Yat-Sen University China

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate item. We argue that this static encoding scheme is difficult to fully capture the users' preference. In this paper, we propose a novel context-aware user-item representation learning model for rating prediction, named CARL. Namely, CARL derives a joint representation for a given user-item pair based on their individual latent features and latent feature interactions. Then, CARL adopts Factorization Machines to further model higher-order feature interactions on the basis of the user-item pair for rating prediction. Specifically, two separate learning components are devised in CARL to exploit review data and interaction data respectively: review-based feature learning and interaction-based feature learning. In review-based learning component, with convolution operations and attention mechanism, the relevant features for a user-item pair are extracted by jointly considering their corresponding reviews. However, these features are only reivew-driven and may not be comprehensive. Hence, interaction-based learning component further extracts complementary features from interaction data alone, also on the basis of user-item pairs. The final rating score is then derived with a dynamic linear fusion mechanism. Experiments on five real-world datasets show that CARL achieves significantly better rating predication accuracy than existing state-of-the-art alternatives. Also, with attention mechanism, we show that the relevant information in reviews can be highlighted to interpret the rating prediction. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Network failure–aware redundant virtual machine placement in a cloud data center

Network failure–aware redundant virtual machine placement i...

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作者： Zhou, Ao Wang, Shangguang Hsu, Ching-Hsien Kim, Myung Ho Wong, Kok-Seng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Mathematics and Big Data Foshan University Foshan China Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan School of Software Soongsil University Seoul Korea Republic of

Cloud has become a very popular infrastructure for many smart city applications. A growing number of smart city applications from all over the world are deployed on the clouds. However, node failure events from the cloud data center have negative impact on the performance of smart city applications. Survivable virtual machine placement has been proposed by the researchers to enhance the service reliability. Because of the ignorance of switch failure, current survivable virtual machine placement approaches cannot achieve the best effect. In this paper, we study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Firstly, we formulate the network failure–aware redundant virtual machine placement problem as an integer nonlinear programming problem and prove that the problem is NP-hard. Secondly, we propose a heuristic algorithm to solve the problem. Finally, extensive simulation results show the effectiveness of our algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Reliability

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Two-phase locality-sensitive hashing for privacy-preserving distributed service recommendation 1

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Qi, Lianyong Dou, Wanchun Zhang, Xuyun School of Information Science and Engineering Qufu Normal University Jining China Chinese Academy of Education Big Data Qufu Normal University Jining China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the "similar friends" of a target user through a privacy-preserving two-phase LSH process;afterwards, we determine the services preferred by the "similar friends" of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. © 2017, Springer International Publishing AG.

关键词： Efficiency

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Cave Recognition Under Tensor Flow Platform

Cave Recognition Under Tensor Flow Platform

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2nd International Conference on Artificial Intelligence and engineering Applications(AIEA 2017)

作者： MIN ZHAO CHANGJIANG WEI SHUHUA WANG School of Data Science and Software Engineering Qingdao University West Branch Research Institute of Petroleum Exploration and Development Shengli Oilfield SINOPEC

Based on the analysis network of Tensor flow’s advantage parameters of to depth realize learning,the this paper constructs CNN's layer and its key on of classification platform and recognition *** of underground caverns Tensor flow frame Tarim by CNN Firstly,and the classification classification the cave in the Basin of Xinjiang training is out,the result board is taken as the input and of the *** Secondly,learning by rate,analyzing network the layer tensor characteristics,training constantly and optimize get and other model parameters,finally a higher precision ***,the that is used to predict the unknown *** experimental accuracy,results show weight the method and the process can obtain high well,model have and the parameters the and bias parameters new can of converge cave a in higher widen prediction the rate for cave,innovate areas,a method identification,depth and tensor flow’s industry application application will greatly advance the of learning the exploration and development.

关键词： Tensor flow Convolution Neural Network Cave Identification

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Image Super-resolution Reconstruction Algorithm Based on Bayesian Theory

Image Super-resolution Reconstruction Algorithm Based on Bay...

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IEEE Conference on Industrial Electronics and Applications

作者： Wenbo Zheng Fei Deng Shaocong Mo Xin Jin Yili Qu Jiangwei Zhou Rui Zou Jia Shuai Zefeng Xie Sijie Long Chengfeng Zheng School of Software Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Technology Wuhan University of Technology Wuhan China Zhejiang University Hangzhou Zhejiang CN School of Management Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China School Of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Technology Zhejiang University Ningbo China

The Bayesian theory provides a new solution to image super-resolution reconstruction. In view of the poor robustness to noise and motion estimation in the vast majority of super-resolution reconstruction algorithms. In this paper, we propose an image super-resolution reconstruction algorithm based on Bayesian representation. In the proposed algorithm, uncharted super-resolution images, motion parameters and unknown model parameters are utilized for modeling in a hierarchical Bayesian framework. We adopt degenerate distribution to derive the estimation of analytic solutions and applied the solutions to the super-resolution reconstruction which also enables the proposed algorithm robust to noises. The experimental results show that the proposed image super-resolution reconstruction algorithm based on Bayesian representation can achieve higher (or similar) performance than the stateof-the-art methods.

关键词： Similarity Hyper-parameter Bayesian Sparse image

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Multi-User Revocable Keyword Search Scheme for

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Journal of Physics: Conference Series 2018年第3期1087卷

作者： Liu Mei Tan Xiao Lei Dong Zhen Fu Cao Jia Chen Shen School of Computer Science and Software Engineering East China Normal University Shanghai Shanghai 200062 China Secure Data Sharing in Cloud

With the rapid development of cloud computing, more and more users store their data in the cloud. Considering the privacy problem of the data sharing in the cloud, searchable encryption makes it possible to efficiently share and retrieve data in multi-user settings. The existing keyword search scheme of sharing the private key with authorized users to generate trapdoors may increase the risk of key exposure, and the scheme of encrypting the data with the secret key of each authorized user and generating a ciphertext for each user may not be suitable for data sharing between a large number of users. In this paper, we combine the secure IBBE system with the keywords search and propose the scheme to achieve safe and efficient multi-user data sharing in the cloud. Authorized users can perform keyword search directly on encrypted data without sharing the secret key, and for a shared file, the data owner only needs to encrypt one ciphertext for all authorized users. Besides, authorized users of shared files can be revoked. User authorization tables and IBBE ensure that revoked users cannot access files.

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